Fetching the paper…
Reading the bibliography…
We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations.
Cognitive psychology and its implications
J. R. Anderson · 1990
Earlier work this paper cites.
Principles of object perception
E. S. Spelke · 1990
Earlier work this paper cites.
Structure and interpretation of computer programs
H. Abelson, G. J. Sussman, and J. Sussman · 1996
Earlier work this paper cites.
Physics-based visual understanding
M. Brand · 1997
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Neuroanimator: Fast neural network emulation and control of physics-based models
R. Grzeszczuk, D. Terzopoulos, and G. Hinton · 1998
Earlier work this paper cites.
Dynamics of target selection in multiple object tracking (mot)
Z. W. Pylyshyn and V. Annan · 2006
Earlier work this paper cites.
Program synthesis by sketching
A. Solar-Lezama · 2008
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
Earlier work this paper cites.
The recurrent temporal restricted boltzmann machine
I. Sutskever, G. E. Hinton, and G. W. Taylor · 2009
Earlier work this paper cites.
Torch7: A matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
Earlier work this paper cites.
Internal physics models guide probabilistic judgments about object dynamics
J. Hamrick, P. Battaglia, and J. B. Tenenbaum · 2011
Earlier work this paper cites.
Transforming auto-encoders
G. E. Hinton, A. Krizhevsky, and S. D. Wang · 2011
Earlier work this paper cites.
Dynamic pooling and unfolding recursive autoencoders for paraphrase detection
R. Socher, E. H. Huang, J. Pennington, A. Y. Ng, and C. D. Manning · 2011
Earlier work this paper cites.
Noisy newtons: Unifying process and dependency accounts of causal attribution
T. Gerstenberg, N. Goodman, D. A. Lagnado, and J. B. Tenenbaum · 2012
Earlier work this paper cites.
Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
Earlier work this paper cites.
Physics 101: Learning physical object properties from unlabeled videos
J. Wu, J. J. Lim, H. Zhang, J. B. Tenenbaum, and W. T. Freeman · 2012
Cited alongside, same era.
Simulation as an engine of physical scene understanding
P. W. Battaglia, J. B. Hamrick, and J. B. Tenenbaum · 2013
Cited alongside, same era.
Sources of uncertainty in intuitive physics
K. A. Smith and E. Vul · 2013
Cited alongside, same era.
http://brm.io/matter-js, 2014
L. Brummitt · 2014
Cited alongside, same era.
Inverse graphics with probabilistic cad models
T. D. Kulkarni, V. K. Mansinghka, P. Kohli, and J. B. Tenenbaum · 2014
Cited alongside, same era.
Learning physics from dynamical scenes
T. Ullman, A. Stuhlmüller, and N. Goodman · 2014
Cited alongside, same era.
Social lstm: Human trajectory prediction in crowded spaces
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese · 2016
Closest in time.
Learning to compose neural networks for question answering
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
Closest in time.
Interaction networks for learning about objects, relations and physics
P. Battaglia, R. Pascanu, M. Lai, D. Jimenez Rezende, and K. Koray · 2016
Closest in time.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Closest in time.
Attend, infer, repeat: Fast scene understanding with generative models
S. Eslami, N. Heess, T. Weber, Y. Tassa, K. Kavukcuoglu, and G. E. Hinton · 2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Humans predict liquid dynamics using probabilistic simulation
C. J. Bates, I. Yildirim, J. B. Tenenbaum, and P. W. Battaglia · 2015
Cited alongside, same era.
Unsupervised learning by program synthesis
K. Ellis, A. Solar-Lezama, and J. Tenenbaum · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
Cited alongside, same era.
rnn: Recurrent library for torch
N. Léonard, S. Waghmare, and Y. Wang · 2015
Cited alongside, same era.
Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2015
Cited alongside, same era.
Newtonian image understanding: Unfolding the dynamics of objects in static images
R. Mottaghi, H. Bagherinezhad, M. Rastegari, and A. Farhadi · 2015
Cited alongside, same era.
C. Finn, I. Goodfellow, and S. Levine · 2016
Closest in time.
Terpret: A probabilistic programming language for program induction
A. L. Gaunt, M. Brockschmidt, R. Singh, N. Kushman, P. Kohli, J. Taylor, and D. Tarlow · 2016
Closest in time.
Probabilistic models of cognition, 2016
N. D. Goodman and J. B. Tenenbaum · 2016
Closest in time.
Structural-rnn: Deep learning on spatio-temporal graphs
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena · 2016
Closest in time.
Building machines that learn and think like people
B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman · 2016
Closest in time.
Learning physical intuition of block towers by example
A. Lerer, S. Gross, R. Fergus, and J. Malik · 2016
Closest in time.
To fall or not to fall: A visual approach to physical stability prediction
W. Li, S. Azimi, A. Leonardis, and M. Fritz · 2016
Closest in time.
” what happens if…” learning to predict the effect of forces in images
R. Mottaghi, M. Rastegari, A. Gupta, and A. Farhadi · 2016
Closest in time.
Understanding visual concepts with continuation learning
W. F. Whitney, M. Chang, T. Kulkarni, and J. B. Tenenbaum · 2016
Closest in time.
Intuitive physics
K. Fragkiadaki, P. Agrawal, S. Levine, and J. Malik · 2017
Closest in time.